Quantitative Analysis of Contrast-Enhanced Ultrasound Images of Brain-Dead Donor Livers: Prediction of Early Allograft Dysfunction.

Abstract

Objective: To determine the CEUS parameters that predict the likelihood of postoperative EAD.

Methods: Clinical and imaging data for 75 pairs of donors and recipients collected between September 2022 and July 2023 were retrospectively analyzed. Subjects were divided into those with early allograft dysfunction (EAD) and those without EAD. The liver parenchyma was selected as the region of interest to plot the CEUS time-intensity curve. CEUS parameters were compared between the two groups.

Results: Peak intensity, area under the curve (AUC), and cholinesterase values were significantly lower in the EAD group than in the non-EAD group. The hepatic arterial-portal arrival interval (APAI) and aspartate aminotransferase level were significantly higher in the EAD group. Multivariate logistic analysis identified the APAI to be an independent risk factor for EAD (odds ratio 0.755; 95% confidence interval 0.577-0.989; p = 0.041). Receiver-operating characteristic curve analysis showed that the prediction probability P, which represents a combination of CEUS and clinical data, was best able to predict EAD (AUC 0.802; 95% confidence interval 0.679-0.926; p < 0.0001). Comparison of the AUC for prediction probability P and each single parameter identified statistically significant differences between the predicted probability P and aspartate aminotransferase and cholinesterase values (p = 0.042 and p = 0.020, respectively).

Conclusion: A longer APAI can be used as a biomarker to predict EAD after brain-dead donor liver transplantation. CEUS could be a valuable tool for assessment of donor livers and identifying recipients potentially at risk of developing postoperative EAD.